my photo

Panagiotis Tsinganos

Senior Machine Learning Engineer | Edge AI & Signal Processing Specialist

Panagiotis Tsinganos is a Machine Learning Engineer and Researcher with a PhD in Deep Learning and Bio-signal Processing. He specializes in developing state-of-the-art AI models for real-time applications, with extensive experience in Computer Vision, telematics, and high-performance edge deployment (Android/Native). His work bridges the gap between advanced research in signal processing and scalable, production-grade software engineering.

Contact me

Experience

dreyev

Software Engineer • Jan, 2022 — Jan, 2026

Architected and optimized edge-based Computer Vision systems for real-time driver attention management.

  • Inference Optimization: Replaced standard TFLite interpreters with a custom native C++ engine using the ncnn framework, reducing model latency by 50% and enabling 15FPS road object detection and analysis on mid-range Android devices.
  • Model Engineering: Enhanced detection accuracy by 18% through targeted dataset annotation and fine-tuning of TensorFlow models for complex driver state monitoring tasks.
  • Sensor Fusion: Designed and implemented multi-modal driving score algorithms by fusing high-frequency IMU, GPS, and CV event streams to provide real-time safety feedback.
  • Pipeline Management: Orchestrated the end-to-end telematics data pipeline, managing the collection and processing of real-time driving data.

Digital Signal and Image Processing Lab, University of Patras

Teaching Assistant • Dec, 2017 — Dec, 2025

Mentored the next generation of engineers in AI and Signal Processing.

  • Student Mentorship: Supervised 10+ undergraduate theses, leading to 5 published conference papers and successful industry placements for students.
  • Curriculum Guidance: Developed and taught hands-on labs for Machine Learning and Digital Signal Processing using Python and MATLAB, improving student project completion rates.

Digital Signal and Image Processing Lab, University of Patras

Researcher • Jul, 2017 — Dec, 2022

Lead researcher on Deep Learning for biomedical signal processing and pattern recognition.

  • Hand Gesture Recognition: Developed novel Temporal Convolutional Network (TCN) architectures in Tensorflow, achieving 90% classification accuracy in descriminating between 53 classes.
  • Real-time Rehabilitation: Engineered a low-latency gesture control interface (sub-100ms) for the ENHANCE project, directly improving user engagement in clinical trials.
  • Academic Impact: Published 5+ peer-reviewed papers in high-impact journals (Sensors MDPI) and presented at international conferences (ICASSP, IISA).
  • International Collaboration: Successfully coordinated research initiatives with Vrije Universiteit Brussels, resulting in a joint PhD (Cotutelle) and shared datasets.

Skills

Programming languages

Python, Java/Kotlin (Android), Matlab, HTML, JavaScript, C/C++

Machine Learning & AI

TensorFlow, PyTorch, Scikit-learn, MLflow, Deep Learning, Model Optimization, Quantization, Inference, Transfer Learning

Edge AI & Embedded ML

ncnn, ONNX Runtime, TensorFlow Lite, Hardware Acceleration, Mobile ML Deployment

Computer Vision & Sensors

Road Object Detection, Driver State Monitoring, YOLO models, Sensor Fusion (IMU, GPS), Telematics

Cloud

Docker

Mobile Development

Android SDK/NDK, JNI, Native code optimization

Signal Processing

Image Processing, Digital Signal Processing, Biomedical Signals (sEMG, ECG), Wearable Sensors

Web Development

Flask, REST API, React

Communication protocols

TCP/IP, GSM, MQTT

Education

University of Patras, Greece – Vrije Universiteit Brussels, Belgium

Doctor of Philophy and Doctor of Engineering Sciences (Joint Phd / Cotutelle) • 12/2017 — 11/2021

Awarded as part of a joint doctoral program (Cotutelle) between the Department of Electrical and Computer Engineering (UPatras) and the Department of Electronics and Informatics (VUB-ETRO).

  • Andreas Mentzelopoulos Scholarships for the University of Patras

Thesis title: «Multi-channel EMG pattern classification based on deep learning»

University of Patras, Greece

MSc Degree in Biomedical Engineering • 10/2015 — 6/2017

Thesis title: «Smartphone-based fall detection system for the elderly»

Grade: 9.02/10

University of Patras, Greece

Diploma in Electrical and Computer Engineering • 10/2010 — 10/2015

Thesis title: «Transmission of biomedical signals using a wireless sensor network»

Grade: 8.31/10

Certifications

AI-Powered Software Engineer

Udacity • 2026

Completed the Udacity Nanodegree program focused on integrating AI into the software development lifecycle, utilizing LLMs for code generation, and building AI-enhanced applications.

PyTorch for Deep Learning

DeepLearning.AI • 2026

Mastered fundamental and advanced concepts of deep learning using the PyTorch framework.

MCP: Build Rich-Context AI Apps with Anthropic

DeepLearning.AI • 2026

Learned the core concepts of MCP and how to build AI applications using it.

Building Coding Agents with Tool Execution

DeepLearning.AI • 2025

Acquired advanced skills in developing autonomous agents capable of interacting with APIs and tools.

Udacity AI for Healthcare Nanodegree

Udacity • 2020

Completed a nanodegree focusing on AI applications in healthcare.

Udacity Full Stack Developer Nanodegree

Udacity • 2020

Completed a nanodegree covering front-end and back-end web development.

Publications

P. Tsinganos, B. Jansen, J. Cornelis and A. Skodras, “Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks”, Sensors, MDPI, 22(5), 1694, 2022.

P. Tsinganos, J. Cornelis, B. Cornelis, B. Jansen and A. Skodras, “Transfer Learning in sEMG-based Gesture Recognition”, 2021 International Conference on Information, Intelligence, Systems and Applications (IISA 2021), Chania, Greece, 2021.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals”, International Journal of Electrical and Computer Engineering Systems, 12(1), 2021.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Data Augmentation of Surface Electromyography for Hand Gesture Recognition”, Sensors, MDPI, 20(17), 4892, 2020.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Hilbert sEMG data scanning for hand gesture recognition based on deep learning”, Neural Computing and Applications, Springer, 2020.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Hand Gesture Recognition Based on EMG Data: A Convolutional Neural Network Approach”, Physiological Computing Systems. PhyCS 2016, PhyCS 2017, PhyCS 2018. Lecture Notes in Computer Science, , A. Holzinger, A. Pope and H. Plácido da Silva, Springer, Cham, 2019, pp. 180-197.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “A Hilbert Curve Based Representation of sEMG Signals for Gesture Recognition”, 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), Osijek, Croatia, 2019, pp. 201-206.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Improved Gesture Recognition Based on sEMG Signals and TCN”, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 2019, pp. 1169–1173.

P. Tsinganos, A. Skodras, B. Cornelis and B. Jansen, “Deep Learning in Gesture Recognition Based on sEMG Signals”, Learning Approaches in Signal Processing, 1st ed., F. Ring, W.-C. Siu, L.-P. Chau, L. Wang and T. Tang, Eds. Pan Stanford Publishing, 2018, pp. 471.

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras, “Deep Learning in EMG-based Gesture Recognition”, 5th International Conference on Physiological Computing Systems (PhyCS), Seville, Spain, 2018, pp. 107–114.

P. Tsinganos and A. Skodras, “On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique Fall Detection”, Sensors, MDPI, 18(2), 592, 2018.

P. Tsinganos and A. Skodras, “A Smartphone-based Fall Detection System for the Elderly”, 10th International Symposium on Image and Signal Processing and Analysis (ISPA), Ljubljana, Slovenia, 2017, pp. 53-58.

Projects

Researcher - Developer • Sep. 2020

Supervised the development of a serious game controlled by a surface electromyography (sEMG) interface for rehabilitation purposes

Researcher - Developer • Sep. 2016 — Jun. 2017

Implemented an Android app that detects when an elderly user has fallen and automatically alerts their selected emergency contacts - Developed a sensor fusion algorithm in Java/Android that integrated accelerometer and gyroscope data to distinguish between "Activities of Daily Living" (ADLs) and actual falls.

Recognition

Best Student Paper

IEEE, EURASIP, University of Osijek, FERIT • 2019

Awarded for the paper with title “A Hilbert Curve Based Representation of sEMG Signals for Gesture Recognition” presented in IWSSIP 2019 Osijek, Croatia.

Outside Interests

  • Photography
  • Painting
  • Cycling
  • Music

Social Links

  • Github: https://github.com/ptsinganos
  • LinkedIn: https://gr.linkedin.com/in/ptsinganos